Loading and cleaning the data.

airplane_df = read_csv("datasets/airplane_crashes_data.csv") |> 
  janitor::clean_names() |> 
  filter(ground != "NULL", aboard != "NULL") |> 

  # removes unnecessary columns for our analyses
  select(-flight_number, -time, - registration)
## Rows: 5268 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): Date, Time, Location, Operator, Flight #, Route, Type, Registratio...
## dbl  (3): Aboard, Fatalities, Ground
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Converting 2 digit years to four digit years.

airplane_df = airplane_df |> 
  mutate(
    # remove leading and trailing spaces
    date = str_trim(date),
    
    # extract month, day, and year from the date string
    m = as.numeric(sub("/.*", "", date)),                    
    d = as.numeric(sub(".*/(.*)/.*", "\\1", date)),          
    y = as.numeric(sub(".*/(.*)$", "\\1", date)),            
    
    # convert 2-digit years (<100) to 4-digit (1900s)
    y = ifelse(y < 100, y + 1900, y),
    
    # rebuild the string
    date_clean = paste(m, d, y, sep = "/"),
    
    # convert to Date type
    date = mdy(date_clean),
    
    # extract numeric year and month
    year = year(date),
    month = month(date),
    month_name = factor(month(date, 
                              label = TRUE, 
                              abbr = TRUE), 
                              levels = month.abb)
  ) |>
  select(-m, -d, -y, -date_clean)  # remove unnecessary columns

Converting variables to their proper variable types.

airplane_df = airplane_df |> 
  mutate(
    aboard     = as.numeric(aboard),
    fatalities =  as.numeric(ground),
    operator   = as.factor(operator)  # to group by operator
    )

Creating a decade column, now that year is numeric.

airplane_df = airplane_df |> 
  mutate(
    decade = floor(year / 10) * 10, 
    decade = paste0(decade, "s")
  ) |> 
  select(date, year, decade, month, month_name, everything())

Exploring the data.

Crashes per year?

# first, using ggplot

airplane_df |> 
  group_by(year) |> 
  summarize(total_crashes = n()) |> 
  ggplot(aes(x = year, y = total_crashes)) + 
  geom_line() +
  geom_point() + 
  geom_smooth(span = 0.2, color = "red", se = FALSE) +
  labs(
    title = "Airplane Crashes per Year",
       x  = "Year",
       y  = "Number of Crashes"
    )
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Interactive plot of crashes per year with plotly.

airplane_df |> 
  count(year) |> 
  plot_ly(
    x = ~year, 
    y = ~n,
    type = "bar",
    mode = "lines+markers",
    hovertemplate = "Year: %{x}<br>Crashes: %{y}<extra></extra>"
  ) |> 
  layout(
    title = list(
      text = "Airplane Crashes per Year",
      y    = 0.98,
      font = list(size = 20)
    ),
      xaxis  = list(
      title  = list(
        text = "Year",
        font = list(size = 16)
      )
      ),
      yaxis  = list(
      title  = list(
        text = "Number of Crashes",
        font = list(size = 16)
      )
    )
  )

Seasonal trends, combining all years?

airplane_df |> 
  group_by(month_name) |> 
  summarize(total_crashes = n()) |> 
  ggplot(aes(x = month_name, y = total_crashes)) +
  geom_col(fill = "blue") +
  labs(title = "Airplane Crashes by Month")

Top airlines with crashes?

airplane_df |> 
  group_by(operator) |> 
  summarise(total_crashes = n(), .groups = "drop") |> 
  slice_max(total_crashes, n = 15) |>  # select the top 15 operators
  ggplot(aes(x = reorder(operator, total_crashes), y = total_crashes )) +
  geom_col(fill = "blue") +
  coord_flip() +
  labs(
    title = "Top 15 Airline Operators with the Most Crashes",
    x     = "Airline Operators",
    y     = "Number of Crashes"
  )  + 
  theme(
    axis.title.y = element_text(margin = margin(r = 20))
  )

Looking at the number of fatalities per year. I have excluded the year 2001, which was an outlier and had 5,641 fatalities, because of the 9/11 attacks.

# summarize the fatalities by year
fatalities_per_year = airplane_df |> 
  group_by(year) |> 
  summarize(total_fatalities = sum(fatalities, na.rm = TRUE)) |> 
  arrange(year) |> 
  filter(year != 2001) # removes the 2001 outlier for plotting
  
  # plot the values
ggplot(fatalities_per_year, aes(x = year, y = total_fatalities)) +
geom_col(color = "darkblue") +
labs(
  title = "Total Fatalities from Airplane Crashes per Year",
  x = "Year",
  y = "Number of Fatalities"
  )